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Recent advancements in computer science include some optimization models that have been developed and used in real applications. Some metaheuristic search/optimization algorithms have been tested to obtain optimal solutions to speed controller applications in self-driving cars. Some metaheuristic algorithms are based on social behaviour, resulting in several search models, functions, and parameters, and thus algorithm-specific strengths and weaknesses. The present paper proposes a fitness function on the basis of the mathematical description of proportional integrative derivate controllers showing that mean square error is not always the best measure when looking for a solution to the problem. The fitness developed in this paper contains features and equations from the mathematical background of proportional integrative derivative controllers to calculate the best performance of the system. Such results are applied to quantitatively evaluate the performance of twenty-one optimization algorithms. Furthermore, improved versions of the fitness function are considered, in order to investigate which aspects are enhanced by applying the optimization algorithms. Results show that the right fitness function is a key point to get a good performance, regardless of the chosen algorithm. The aim of this paper is to present a novel objective function to carry out optimizations of the gains of a PID controller, using several computational intelligence techniques to perform the optimizations. The result of these optimizations will demonstrate the improved efficiency of the selected control schema., Author(s): Luis Fernando de Mingo López [1]; Francisco Serradilla García [1,2]; José Eugenio Naranjo Hernández (corresponding author) [1,2]; Nuria Gómez Blas [1] 1. Introduction Many optimization problems are nondeterministic polynomial-time [...]

This paper introduces a new notion of a Fenchel conjugate, which generalizes the classical Fenchel conjugation to functions defined on Riemannian manifolds. We investigate its properties, e.g., the Fenchel-Young inequality and the characterization of the convex subdifferential using the analogue of the Fenchel-Moreau Theorem. These properties of the Fenchel conjugate are employed to derive a Riemannian primal-dual optimization algorithm and to prove its convergence for the case of Hadamard manifolds under appropriate assumptions. Numerical results illustrate the performance of the algorithm, which competes with the recently derived Douglas-Rachford algorithm on manifolds of nonpositive curvature. Furthermore, we show numerically that our novel algorithm may even converge on manifolds of positive curvature., Author(s): Ronny Bergmann [sup.1], Roland Herzog [sup.1], Maurício Silva Louzeiro [sup.1], Daniel Tenbrinck [sup.2], José Vidal-Núñez [sup.3] Author Affiliations: (1) grid.6810.f, 0000 0001 2294 5505, Faculty of Mathematics, Technische Universität [...]

To improve the capacity of metro systems, it is important to evaluate and minimise headway, which is defined as the time interval calculated from “head to head” between two successive trains in this paper. With existing approaches for headway optimisation, the headway for moving block systems is often calculated based on the safe braking distance. However, the blocking time at movable elements (e.g., switches and crossings) and stops has special characteristics. Since train separation is dominated by a signalling system, the distance between two successive trains at movable elements and stops exceeds the safe braking distance. In this work, the theory for building a blocking time model and calculating line headway for moving block systems is investigated. A workflow to minimise line headway is designed to derive an optimised velocity profile before the identified bottlenecks. Several different optimisation algorithms, including grid search, Monte Carlo, and simulated annealing, are developed and compared. Among them, simulated annealing shows the best optimisation capability with the least computational effort. The designed algorithm has been tested for Hefei-Metro Line 1, and the line headway can be reduced from 116.776 seconds to 105.806 seconds. If the acceptable rate of the increased transport is set at 1%, the line capacity will increase by 6.5%., Author(s): Yong Cui (corresponding author) [1,2]; Qing Yu [3]; Chenyang Wang [4] 1. Introduction To improve the capacity of metro systems, it is important to evaluate and minimise headway, which [...]

High-speed trains powered by electricity have low-carbon emissions and are the important intercity travel conveyances conducive to sustainable social development. The sustainable development of high-speed railway needs to improve the operating revenue and the resource utilization. Joint optimization of dynamic pricing and ticket allocation are often used to improve high-speed railway revenue and seat utilization in the literature. However, the uncertainty of demand makes joint decision-making difficult to be accurate. The risk preference of decision-makers also deeply affects the effect of joint decision-making. To cope with the uncertain demand and test the effect of operator risk preference on joint decision-making, this paper used chance constrained programming theory to optimize dynamic pricing and ticket allocation simultaneously. The presale period is divided into several stages. In each stage, ticket demand follows the normal distribution and changes with the price elasticity. Passengers choose tickets according to the multinomial logit model. The chance constrains reflect operators' risk preference. The chance constrained stochastic nonlinear programming is proposed for joint decision of dynamic pricing and ticket allocation. Then, the combination algorithm of particle swarm optimization algorithm and mixed-integer linear programming was designed to solve the model. Finally, the numerical experiments according to actual operating scale were design to validate the model and algorithm. The results indicate that under different confidence levels, the proposed model and algorithm increase the total revenue by 11.84%-13.40% compared with the ticket allocation under the single fixed fare. The model can help the high-speed railway operators understand the impact of risk preference on joint decision-making, and provide decision support for them., Author(s): Huaqin Meng [1]; Zhenying Yan (corresponding author) [1]; Yu Wang [1]; Yiwei Xu [1] 1. Introduction With the growth of social economy and breakthrough of technological innovation, the high-speed [...]

This study proposes a two-level optimization model system for vehicle control and signal timing at isolated signal intersections under the mixed traffic flow environment composed of intelligent connected autonomous vehicles (CAVs) and connected human-driven vehicles (CHVs), to minimize the energy consumption and vehicle delay at intersections. The proposed two-layer optimization model is composed of a two-layer vehicle trajectory control model and a fuzzy control signal timing optimization model. The two-layer vehicle trajectory control model includes a signal-oriented vehicle trajectory control model and a car-following oriented vehicle trajectory control model. The former calculates expected acceleration and speed commands at each time step according to the coming signal information, to help vehicle pass the closest signal intersection without stopping during the green light interval; the latter uses the variable headway (VTH) strategy to follow the preceding vehicle by maintaining a safe distance. A microscopic simulator based on SUMO is developed to test the performance of the proposed optimization algorithm. In the simulation experiment, with the driving characteristics of CHV drivers considered, the results show that our model performs well under a CAV penetration rate of 30%-60% and under small or moderate levels of traffic flow. The average waiting time of vehicles is reduced by about 25% compared with the uncontrolled scheme. Under the condition of penetration rate of 60%, the average energy consumption of vehicles in the proposed model is 17.56% lower than that of the uncontrolled scheme. In addition, the proposed model reduces by 21.94% compared with the scheme of only controlling vehicles. When the traffic flow is at a low or medium level, the average energy consumption and waiting time of vehicles are reduced by nearly 35% with the proposed model., Author(s): Yazhu Zou (corresponding author) [1]; Moyan Li [1]; Jiabo Guo [1]; Enjian Yao [1]; Rongsheng Chen [1] 1. Introduction In recent years, intelligent transportation and transportation big data have [...]

Accurately predicting short-term congestions in ship traffic flow is important for water traffic safety and intelligent shipping. We propose a method for predicting the traffic flow of ships by applying the whale optimization algorithm to an extreme learning machine. The method considers external environmental uncertainty and complexity of ships navigating in traffic-intensive waters. First, the parameters of ship traffic flow are divided into multiple modal components using variational mode decomposition and extreme learning machine. The machine and the whale optimization algorithm constitute a hybrid modelling approach for predicting individual modal components and integrating the results of individual components. Considering a map between ship traffic flow parameters and congestion, fuzzy c-means clustering is used to predict the level of ship traffic congestion. To verify the effectiveness of the proposed method, ship traffic flow data of the Yangtze River estuary were selected for evaluation. Results from the proposed method for predicting ship traffic flow parameters are consistent with measurements. Specifically, the prediction accuracy of the ship traffic congestion reaches 76.04%, which is reasonable and practical for predicting ship traffic congestion., Author(s): Yongjun Chen [1]; Ming Huang (corresponding author) [2,3,4]; Kaixuan Song [1]; Tengfei Wang [4] 1. Introduction The booming growth in trade has led to the development of the waterway [...]

Multimodal transport is a process of effectively moving cargoes in a single container by combining land transport (road or rail) and maritime or river transport (vessel or barge) in one transport chain. However, cold chain logistics (CCL), as a special while major kind of cargo delivery, has not been incorporated with this beneficial combination. In order to realize efficient delivery of cold chain foods (CCF), in this study, the characteristics of multimodal and CCL are analyzed and integrated to select the optimal logistics path. In establishing the path-selection model, customer satisfaction is introduced, which is reflected by arrival punctuality and the quality of CCF. An improved particle swarm optimization algorithm (IPSO) is introduced to address the model and is proven to retain a fast convergence rate and achieve outstanding solving accuracy through the experimental study. Sensitivity analysis is also conducted to present the impact of railway speed and cost variation on path selection. Results show that compared with highway transport, railway transport is preferable to the medium and long distance. The influence of railway speed improvement is more striking than cost reduction in motivating decision makers to choose railway transport mode in logistics operations., Author(s): Changjiang Zheng [1]; Kai Sun [1]; Yuhang Gu [1]; Jinxing Shen (corresponding author) [1]; Muqing Du [1] 1. Introduction Nowadays, accompanied with an improved quality of life, the demand [...]

The implementation of ship speed control is extremely important in the shipping industry. It is affected by various factors, such as water depth, obstacles, and environmental factors. Traditional speed control methods only consider geographical constraints, which is difficult to achieve the goal of safe navigation and maritime traffic efficiency simultaneously. Accordingly, a two-stage speed dynamic control model is proposed in this study. In the first stage, certain safety navigation factors, including obstacles, sea environment conditions, and limit of estimated time of arrival to destination port, are considered. In the second stage, the speed dynamic control model considering safety and environmental factors is established by combining multisource data and particle swarm optimisation algorithm. The model's superiority and advantage are validated by experiments conducted on an ocean-going ship. The experimental results show that the proposed dynamic speed control model can reduce the ship's fuel consumption and improve energy efficiency while ensuring the safety navigation. The study is anticipated to be used as a reference for speed dynamic control., Author(s): Tianrui Zhou [1]; Qinyou Hu (corresponding author) [1]; Zhihui Hu [1]; Jiamao Zhi [2] 1. Introduction Marine transportation is efficient because more than 80% of goods delivered to various [...]